---
title: "TERN-701 CARDINAL Phase 1 Study Analysis"
subtitle: "Novel Allosteric BCR::ABL1 Inhibitor in Previously Treated CML"
author: "Clinical Analysis Report"
date: "`r Sys.Date()`"
format:
html:
toc: true
toc-depth: 3
toc-location: left
theme: journal
code-fold: true
code-tools: true
fig-width: 10
fig-height: 6
df-print: paged
execute:
warning: false
message: false
---
```{r setup}
#| include: false
# Load required libraries
library(tidyverse)
library(gt)
library(gtsummary)
library(patchwork)
library(plotly)
library(viridis)
library(scales)
library(kableExtra)
# Set theme
theme_set(theme_minimal(base_size = 12))
# Color palette for consistency
cml_colors <- c(
"TERN-701" = "#2E86AB",
"Response" = "#A23B72",
"Safety" = "#F18F01",
"Prior Treatment" = "#6C5B7B"
)
```
## Executive Summary
```{r summary-data}
# Create summary metrics
summary_metrics <- tibble(
Metric = c("Enrolled Patients", "Median Age (years)", "Median Prior TKIs",
"On Treatment", "MMR by 24 weeks", "Grade ≥3 TEAEs"),
Value = c("55", "58 (29-86)", "3 (1-6)",
"48 (87.3%)", "24/32 (75%)", "≤10% each"),
Category = c("Enrollment", "Demographics", "Prior Treatment",
"Retention", "Efficacy", "Safety")
)
summary_metrics %>%
gt(groupname_col = "Category") %>%
tab_header(
title = "CARDINAL Study Key Metrics",
subtitle = "Data cutoff: June 30, 2025"
) %>%
cols_label(
Metric = "Metric",
Value = "Value"
) %>%
tab_style(
style = cell_fill(color = "#E8F4F8"),
locations = cells_body(columns = everything(), rows = Category == "Efficacy")
) %>%
tab_style(
style = cell_text(weight = "bold"),
locations = cells_body(columns = Metric)
) %>%
tab_options(
table.font.size = 14,
heading.title.font.size = 16
)
```
## Study Design
### Dose Escalation Cohorts
```{r dose-cohorts}
# Create dose escalation data
dose_data <- tibble(
Cohort = 1:4,
Dose = c(160, 320, 400, 500),
`Dose Level` = paste0(Dose, " mg QD"),
Status = c("Complete", "Complete", "Complete", "Complete"),
RDE = c("No", "Yes", "No", "Yes")
)
dose_data %>%
gt() %>%
tab_header(title = "Part 1: Dose Escalation") %>%
cols_label(
Cohort = "Cohort",
Dose = "Dose (mg)",
`Dose Level` = "Dosing",
Status = "Status",
RDE = "Selected for Expansion"
) %>%
tab_style(
style = cell_fill(color = "#90EE90"),
locations = cells_body(
columns = everything(),
rows = RDE == "Yes"
)
) %>%
tab_footnote(
footnote = "RDE = Recommended Dose for Expansion",
locations = cells_column_labels(columns = RDE)
)
```
## Patient Characteristics
### Demographics and Baseline Disease
```{r patient-characteristics}
# Create patient characteristics data
patient_chars <- tibble(
Characteristic = c("Age, median (range)", "Male sex", "Prior TKIs, median (range)",
"≥4 prior TKIs", "Prior asciminib", "Prior ponatinib/investigational",
"Baseline BCR::ABL1 >1%", "Baseline BCR::ABL1 >10%",
"T315I mutation", "F317L mutation"),
Value = c("58 (29-86)", "31 (56%)", "3 (1-6)", "19 (35%)", "20 (36%)",
"14 (25%)", "31 (56%)", "24 (44%)", "5 (9%)", "2 (4%)"),
N = 55
)
# Visualization of prior treatments
prior_tx_data <- tibble(
`Prior TKIs` = c("1", "2", "3", "4", "5", "6"),
Count = c(8, 12, 16, 10, 6, 3), # Estimated from percentages
Percentage = Count/55*100
)
p1 <- ggplot(prior_tx_data, aes(x = `Prior TKIs`, y = Percentage)) +
geom_col(fill = cml_colors["Prior Treatment"], alpha = 0.8) +
geom_text(aes(label = paste0(round(Percentage, 1), "%")),
vjust = -0.5, size = 3.5) +
labs(title = "Distribution of Prior TKI Experience",
x = "Number of Prior TKIs",
y = "Percentage of Patients (%)") +
ylim(0, 35) +
theme(plot.title = element_text(hjust = 0.5, face = "bold"))
# Prior specific TKIs
prior_specific <- tibble(
Treatment = c("2G TKIs\n(dasatinib, nilotinib, bosutinib)",
"Asciminib",
"Ponatinib/\nInvestigational"),
Percentage = c(100, 36, 25)
)
p2 <- ggplot(prior_specific, aes(x = reorder(Treatment, -Percentage),
y = Percentage)) +
geom_col(fill = c("#2E86AB", "#A23B72", "#F18F01"), alpha = 0.8) +
geom_text(aes(label = paste0(Percentage, "%")),
vjust = -0.5, size = 4) +
labs(title = "Prior Treatment Exposure",
x = "",
y = "Percentage of Patients (%)") +
ylim(0, 110) +
theme(plot.title = element_text(hjust = 0.5, face = "bold"),
axis.text.x = element_text(size = 10))
p1 + p2 + plot_layout(ncol = 2)
```
### Baseline BCR::ABL1 Levels
```{r bcr-abl-levels}
# Create BCR::ABL1 distribution data
bcr_abl_data <- tibble(
Category = c("BCR::ABL1 ≤1%", "BCR::ABL1 1-10%", "BCR::ABL1 >10%"),
Patients = c(24, 7, 24), # Estimated from percentages
Percentage = c(44, 12, 44)
)
# Create donut chart for BCR::ABL1 distribution
plot_ly(bcr_abl_data,
labels = ~Category,
values = ~Patients,
text = ~paste(Patients, "patients\n(", Percentage, "%)", sep = ""),
textposition = 'outside',
textinfo = 'label+text',
marker = list(colors = c("#E8F4F8", "#A8D8EA", "#2E86AB")),
hole = 0.4) %>%
add_pie() %>%
layout(title = list(text = "<b>Baseline BCR::ABL1 Distribution</b>",
font = list(size = 16)),
showlegend = FALSE,
annotations = list(
text = "N=55",
x = 0.5,
y = 0.5,
xref = "paper",
yref = "paper",
showarrow = FALSE,
font = list(size = 20)
))
```
## Efficacy Outcomes
### Major Molecular Response (MMR) by 24 Weeks
```{r efficacy-outcomes}
# Create efficacy data
efficacy_data <- tibble(
Population = c("Overall Evaluable", "Achieved MMR", "Maintained MMR",
"Discontinued for Lack of Efficacy", "Prior Asciminib",
"Prior Asci/Ponat/Invest"),
`N Evaluable` = c(32, 22, 10, 16, 10, 12),
`MMR by 24 weeks` = c(24, 14, 10, 11, 6, 8),
`Response Rate` = c(75, 64, 100, 69, 60, 67)
)
# Create waterfall-style response plot
efficacy_plot_data <- efficacy_data %>%
mutate(Population = factor(Population,
levels = Population[order(`Response Rate`)]))
ggplot(efficacy_plot_data, aes(x = Population, y = `Response Rate`)) +
geom_col(fill = cml_colors["Response"], alpha = 0.8, width = 0.7) +
geom_text(aes(label = paste0(`Response Rate`, "%\n(",
`MMR by 24 weeks`, "/", `N Evaluable`, ")")),
vjust = -0.5, size = 3.5) +
coord_flip() +
labs(title = "MMR Achievement by 24 Weeks Across Patient Subgroups",
x = "",
y = "Response Rate (%)") +
scale_y_continuous(limits = c(0, 110), breaks = seq(0, 100, 20)) +
theme(plot.title = element_text(hjust = 0.5, face = "bold", size = 14),
axis.text.y = element_text(size = 11))
```
### Response Dynamics
```{r response-dynamics}
# Create response tracking table
response_tracking <- tibble(
`Response Metric` = c("Achieved MMR (no baseline MMR)",
"Maintained MMR (baseline MMR)",
"Lost MMR at data cutoff"),
`n/N` = c("14/22", "10/10", "0/24"),
`Rate (%)` = c(64, 100, 0),
`Interpretation` = c("Strong de novo response",
"Excellent maintenance",
"Durable responses")
)
response_tracking %>%
gt() %>%
tab_header(
title = "Response Dynamics Analysis",
subtitle = "Key efficacy endpoints"
) %>%
cols_label(
`Response Metric` = "Metric",
`n/N` = "n/N",
`Rate (%)` = "Rate (%)",
Interpretation = "Clinical Interpretation"
) %>%
tab_style(
style = list(
cell_fill(color = "#90EE90"),
cell_text(weight = "bold")
),
locations = cells_body(
columns = everything(),
rows = `Rate (%)` == 100
)
)
```
## Safety Profile
### Treatment-Emergent Adverse Events (TEAEs)
```{r safety-profile}
# Create safety data
safety_data <- tibble(
`Adverse Event` = c("Any TEAE", "Diarrhea", "Headache", "Nausea",
"Neutropenia (Grade ≥3)", "Thrombocytopenia (Grade ≥3)"),
`All Grades n (%)` = c("41 (74)", "12 (22)", "10 (18)", "9 (16)",
"4 (7)", "2 (4)"),
`Grade ≥3 n (%)` = c("NA", "0", "0", "0", "4 (7)", "2 (4)"),
Category = c("Overall", "GI", "Neurological", "GI",
"Hematologic", "Hematologic")
)
# Visualization of common AEs
ae_plot_data <- tibble(
AE = c("Diarrhea", "Headache", "Nausea", "Neutropenia\n(Gr ≥3)",
"Thrombocytopenia\n(Gr ≥3)"),
Percentage = c(22, 18, 16, 7, 4),
Grade = c("Grade 1-2", "Grade 1-2", "Grade 1-2", "Grade ≥3", "Grade ≥3")
)
ggplot(ae_plot_data, aes(x = reorder(AE, Percentage), y = Percentage, fill = Grade)) +
geom_col(alpha = 0.8) +
geom_text(aes(label = paste0(Percentage, "%")), hjust = -0.2, size = 4) +
coord_flip() +
scale_fill_manual(values = c("Grade 1-2" = "#FFA500", "Grade ≥3" = "#DC143C")) +
labs(title = "Most Common Treatment-Emergent Adverse Events",
x = "",
y = "Percentage of Patients (%)") +
scale_y_continuous(limits = c(0, 30), breaks = seq(0, 30, 5)) +
theme(plot.title = element_text(hjust = 0.5, face = "bold"),
legend.position = "bottom")
```
### Treatment Discontinuations
```{r discontinuations}
# Create discontinuation data
discontinuation_data <- tibble(
Reason = c("Disease Progression", "Adverse Events",
"Consent Withdrawal/Lost to Follow-up"),
Count = c(4, 1, 2),
Percentage = round(Count/55*100, 1)
)
# Create pie chart for discontinuations
plot_ly(discontinuation_data,
labels = ~Reason,
values = ~Count,
text = ~paste(Count, "patients\n(", Percentage, "%)", sep = ""),
textposition = 'outside',
textinfo = 'text',
marker = list(colors = c("#DC143C", "#FFA500", "#808080"))) %>%
add_pie() %>%
layout(title = list(text = "<b>Reasons for Treatment Discontinuation (n=7)</b>",
font = list(size = 14)),
showlegend = TRUE,
legend = list(x = 0.85, y = 0.5))
```
## Comparative Context
### TERN-701 vs Historical Allosteric Inhibitor Data
```{r comparative-analysis}
# Create comparative table (hypothetical comparison with asciminib)
comparative_data <- tibble(
Parameter = c("Dose Range Tested", "RDE Selected", "Prior Asciminib Allowed",
"MMR by 24 weeks (overall)", "MMR in asciminib-exposed",
"Grade ≥3 Neutropenia", "Grade ≥3 Thrombocytopenia"),
`TERN-701\n(CARDINAL)` = c("160-500 mg QD", "320, 500 mg QD", "Yes (36%)",
"75% (24/32)", "60% (6/10)", "7%", "4%"),
`Historical Context` = c("Variable", "Variable", "No/Limited",
"Variable", "Limited data", "Variable", "Variable")
)
comparative_data %>%
gt() %>%
tab_header(
title = "TERN-701 Positioning in Allosteric Inhibitor Landscape",
subtitle = "Key differentiating features"
) %>%
cols_label(
Parameter = "",
`TERN-701\n(CARDINAL)` = "TERN-701 (CARDINAL)",
`Historical Context` = "Historical Context"
) %>%
tab_style(
style = cell_fill(color = "#E8F4F8"),
locations = cells_body(
columns = `TERN-701\n(CARDINAL)`,
rows = grepl("MMR|asciminib", Parameter)
)
) %>%
tab_style(
style = cell_text(weight = "bold"),
locations = cells_body(columns = Parameter)
)
```
## Key Takeaways
```{r key-takeaways}
takeaways <- tibble(
Category = c("Efficacy", "Efficacy", "Safety", "Safety", "Population", "Next Steps"),
Finding = c(
"75% MMR rate by 24 weeks in heavily pretreated population",
"Activity maintained in asciminib-exposed patients (60% MMR)",
"Favorable tolerability with 74% of TEAEs being low grade",
"No DLTs observed, MTD not reached up to 500 mg QD",
"Median 3 prior TKIs with 36% having prior asciminib exposure",
"320 mg and 500 mg QD selected as RDEs for randomized expansion"
),
Significance = c(
"High",
"High",
"Moderate",
"Moderate",
"High",
"High"
)
)
takeaways %>%
gt(groupname_col = "Category") %>%
tab_header(
title = "Clinical Significance Summary"
) %>%
cols_label(
Finding = "Key Finding",
Significance = "Clinical Impact"
) %>%
tab_style(
style = cell_text(weight = "bold", color = "#2E86AB"),
locations = cells_body(
columns = Significance,
rows = Significance == "High"
)
) %>%
tab_style(
style = cell_fill(color = "#F0F8FF"),
locations = cells_row_groups()
)
```
## Conclusions
::: callout-note
## Study Implications
TERN-701 demonstrates promising efficacy and safety in a heavily pretreated CML-CP population, including patients with prior exposure to asciminib and other investigational TKIs. The 75% MMR rate by 24 weeks, combined with favorable tolerability profile, supports continued development in the randomized dose expansion phase.
:::
::: callout-important
## Data Limitations
- Phase 1 dose-escalation design with limited sample size (N=55)
- Median follow-up of 4.5 months requires longer-term assessment
- Cross-trial comparisons should be interpreted with caution
- Full safety profile will emerge with expanded enrollment
:::
```{r session-info}
#| echo: false
#| eval: false
sessionInfo()
```
------------------------------------------------------------------------
